2型糖尿病风险因素的早期检测系统 机器学习的利用 - 随机森林
Johannes B Ginting1, Tri Suci1, Chrismis N Ginting1
1Department of Bachelor of Public Health, Faculty of Medicine, Dentistry and Health Sciences, Universitas Prima Indonesia, North Sumatra, Indonesia.
Journal of family & community medicine
|September 7, 2023
概括
使用机器学习识别了2型糖尿病 (DM) 风险因素. 保持血糖水平对于预防DM至关重要,特别是对于那些有家族病史的人来说.
科学领域:
- 公共卫生 公共卫生
- 流行病学 流行病学
- 生物统计学 生物统计学
背景情况:
- 2型糖尿病 (DM) 发病率的增加需要有效的预防和控制策略.
- 机器学习为早期检测非传染性疾病风险因素提供了先进的工具.
- 医疗保健中的技术整合是管理DM相关的发病率和死亡率上升的关键.
研究的目的:
- 开发一种早期检测2型糖尿病风险因素的系统.
- 使用机器学习识别2型糖尿病发病率的显著预测因素.
- 分析用于糖尿病风险评估的二次监测数据.
主要方法:
- 利用了2019-2021年的二次监控数据.
- 采用双变量,多变量统计和随机森林机器学习算法.
- 数据预处理包括清理,规范化和合并三年的记录.
主要成果:
- 确定年龄,性别,家族史的DM/高血压,高血压,高血糖,肥胖和中心肥胖作为显著的风险因素.
- 家庭史是最强的预测因素 (赔率比为15.101).
- 随机森林模型实现了84%的准确性,年龄,血糖水平和BMI作为关键特征.
结论:
- 血糖水平是2型糖尿病发病率中影响最大的因素.
- 具有家族病史,处于非生产年龄,女性和超重的个人可以通过控制血糖来降低风险.
- 定期监测血糖对于预防糖尿病至关重要,特别是在高危人群中.
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